Low-latency hierarchical routing of reconfigurable neuromorphic systems
Samalika Perera, Ying Xu, André van Schaik, Runchun Wang

TL;DR
This paper introduces a new routing architecture for FPGA-based spiking neural network simulations that reduces latency and improves performance.
Contribution
A novel hierarchical tree-based routing architecture with stochastic arbitration is proposed to reduce latency in multi-FPGA systems.
Findings
The hierarchical tree-based architecture maintains constant local bandwidth while being scalable.
Stochastic arbitration reduces FIFO congestion and improves worst-case latency compared to round-robin methods.
Performance measurements confirm lower latency and better overall performance with the proposed routing scheme.
Abstract
A reconfigurable hardware accelerator implementation for spiking neural network (SNN) simulation using field-programmable gate arrays (FPGAs) is promising and attractive research because massive parallelism results in better execution speed. For large-scale SNN simulations, a large number of FPGAs are needed. However, inter-FPGA communication bottlenecks cause congestion, data losses, and latency inefficiencies. In this work, we employed a hierarchical tree-based interconnection architecture for multi-FPGAs. This architecture is scalable as new branches can be added to a tree, maintaining a constant local bandwidth. The tree-based approach contrasts with linear Network on Chip (NoC), where congestion can arise from numerous connections. We propose a routing architecture that introduces an arbiter mechanism by employing stochastic arbitration considering data level queues of First In,…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neuroscience and Neural Engineering · Ferroelectric and Negative Capacitance Devices
